Interpretable multi-label classification model for predicting post-anesthesia care unit complications: a prospective cohort study.

Abstract

Background: There are potential associations between post-anesthesia care unit (PACU) complications that significantly impact enhanced recovery after surgery. Timely identification of these signs is essential for implementing comprehensive, systematic management strategies and delivering personalized anesthetic care. However, relevant studies are currently limited. This study aimed to develop and validate an interpretable multi-label classification model to predict PACU complications concurrently.

Methods: This prospective cohort study enrolled adult patients who underwent general anesthesia and elective surgery and were transferred to the PACU after surgery. The patients were dynamically monitored and evaluated for the occurrence of six common PACU complications: respiratory adverse events, hypothermia, hemodynamic instability, nausea/vomiting, agitation/delirium, and pain. A multi-label classification model was developed on the basis of 16 key features, and a Markov network was embedded to quantify and analyze the association network among these complications. The SHapley Additive exPlanations (SHAP) method was applied to conduct interpretability analysis of the model.

Results: Of the 16,838 total patients, 6,830 (40.6%) experienced at least one complication. In the training cohort, 2,125 (57.0%) patients experienced two or more complications at the same time. The AUCs for the six complications in the three cohorts ranged from 0.735 to 0.914, 0.720 to 0.920, and 0.693 to 0.928, respectively. Respiratory adverse events performed best. Age, gender, BMI, duration of anesthesia, and postoperative analgesia emerged as the five most important features. The relative importance of respiratory adverse events to hemodynamic instability was the highest.

Conclusion: The integration of a multi-label classification model with interpretable methods has significant advantages in simultaneously predicting PACU complications, identifying the risk factors for specific complications, optimizing postoperative resource allocation, and improving patient outcomes.